The Complete Guide to Parameters – ROS 2 Python

In this tutorial, we’ll explore the power of parameters in ROS 2. Parameters are important for creating flexible and dynamic robot software. 

You’ll learn how to define, access, and use parameters within your nodes, enabling you to customize your robot’s behavior without altering its code. 

Join me as we dive into the essentials of parameters, setting the foundation for more complex robotics applications.

Real-World Applications

Here are some real-world examples of how ROS 2 parameters can be used in robotic arm applications:

  • Joint Limits and Velocities: Parameters can be used to define the safe operating range (minimum and maximum) for each joint of the arm in terms of angles or positions. Additionally, they can set the maximum allowed speed for each joint during movement. This ensures safe and controlled operation, preventing damage to the robot or its surroundings.
  • Payload Capacity: This parameter specifies the maximum weight the arm can safely carry while maintaining stability and avoiding damage to its motors or gears. This parameter is important for tasks like picking and placing objects or performing assembly operations.
  • Path Planning Parameters: These parameters influence how the arm plans its trajectory to reach a target point. They can include factors like joint acceleration/deceleration rates, smoothness of the path, and collision avoidance settings. 
  • End-effector Calibration: Parameters can store calibration data for the gripper or any tool attached to the end of the arm. This data can include offsets, transformations, or specific configurations for different tools, ensuring accurate tool positioning during tasks.

As you can see, pretty much any variable that applies to your robot can be declared as a parameter in ROS 2. Parameters are used to configure nodes at startup (and during runtime), without changing the code.

Remember, a ROS 2 node is a mini-program (written in either Python or C++) that performs a specific task within a robot system. 

Prerequisites

Here is the GitHub repository that contains the code we will develop in this tutorial.

Write the Code

Let’s start by creating a ROS 2 node that declares two parameters (a floating-point value and a string) and includes a function that executes anytime one of the parameters changes.

Open a terminal, and type these commands to open VS Code.

cd ~/ros2_ws
code .

Right-click on src/cobot_arm_examples/scripts folder, and create a new file called “minimal_py_parameters.py”.

Type the following code inside minimal_py_parameters.py:

#! /usr/bin/env python3

"""
Description:
    This ROS 2 node demonstrates how to declare parameters.
-------
Publishing Topics:
    None
-------
Subscription Topics:
    None    
-------
Author: Addison Sears-Collins
Date: March 4, 2024
"""

import rclpy # Import the ROS 2 client library for Python
from rclpy.node import Node # Import the Node class for creating ROS 2 nodes
from rcl_interfaces.msg import ParameterDescriptor # Enables the description of parameters
from rcl_interfaces.msg import SetParametersResult # Handles responses to parameter change requests
from rclpy.parameter import Parameter # Handles parameters within a node

class MinimalParameter(Node):
    """Create MinimalParameter node.

    """
    def __init__(self):
        """ Create a custom node class for declaring parameters.

        """

        # Initialize the node with a name
        super().__init__('minimal_parameter_node')

        # Describe parameters
        velocity_limit_descriptor = ParameterDescriptor(
            description='Maximum allowed angular velocity in radians per second (ignored for fixed joints)')        
        robot_name_descriptor = ParameterDescriptor(description='The name of the robot')

        # Declare parameters
        self.declare_parameter('velocity_limit', 2.792527, velocity_limit_descriptor)
        self.declare_parameter('robot_name', 'Automatic Addison Bot', robot_name_descriptor)

        # Register a callback function that will be called whenever there is an attempt to
        # change one or more parameters of the node.
        self.add_on_set_parameters_callback(self.parameter_change_callback)

    def parameter_change_callback(self, params):
        """Gets called whenever there is an attempt to change one or more parameters.

        Args:
            params (List[Parameter]): A list of Parameter objects representing the parameters that are 
                being attempted to change.
        
        Returns:
            SetParametersResult: Object indicating whether the change was successful.
        """
        result = SetParametersResult()

        # Iterate over each parameter in this node
        for param in params:
            # Check the parameter's name and type
            if param.name == "velocity_limit" and param.type_ == Parameter.Type.DOUBLE:
                # This parameter has changed. Display the new value to the terminal.
                self.get_logger().info("Parameter velocity_limit has changed. The new value is: %f" % param.value)
                # The parameter change was successfully handled.
                result.successful = True
            if param.name == "robot_name" and param.type_ == Parameter.Type.STRING:
                self.get_logger().info("Parameter robot_name has changed. The new value is: %s" % param.value)
                result.successful = True

        return result

def main(args=None):
    """Main function to start the ROS 2 node.

    Args:
        args (List, optional): Command-line arguments. Defaults to None.
    """

    # Initialize ROS 2 communication
    rclpy.init(args=args)

    # Create an instance of the MinimalParameter node
    minimal_parameter_node = MinimalParameter()

    # Keep the node running and processing events.
    rclpy.spin(minimal_parameter_node)

    # Destroy the node explicitly
    # (optional - otherwise it will be done automatically
    # when the garbage collector destroys the node object)
    minimal_parameter_node.destroy_node()

    # Shutdown ROS 2 communication
    rclpy.shutdown()

if __name__ == '__main__':
    # Execute the main function if the script is run directly
    main()

Modify the CMakeLists.txt File

Now let’s configure the CMakeLists.txt file. Here is what it should look like:

cmake_minimum_required(VERSION 3.8)
project(cobot_arm_examples)

# Check if the compiler being used is GNU's C++ compiler (g++) or Clang.
# Add compiler flags for all targets that will be defined later in the 
# CMakeLists file. These flags enable extra warnings to help catch
# potential issues in the code.
# Add options to the compilation process
if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
  add_compile_options(-Wall -Wextra -Wpedantic)
endif()

# Locate and configure packages required by the project.
find_package(ament_cmake REQUIRED)
find_package(ament_cmake_python REQUIRED)
find_package(rclcpp REQUIRED)
find_package(rclpy REQUIRED)
find_package(rcl_interfaces REQUIRED)
find_package(std_msgs REQUIRED)

# Define a CMake variable named dependencies that lists all
# ROS 2 packages and other dependencies the project requires.
set(dependencies
  rclcpp
  rcl_interfaces
  std_msgs
)

# Add the specified directories to the list of paths that the compiler
# uses to search for header files. This is important for C++
# projects where you have custom header files that are not located
# in the standard system include paths.
include_directories(
  include
)

# Tells CMake to create an executable target named minimal_cpp_publisher
# from the source file src/minimal_cpp_publisher.cpp. Also make sure CMake
# knows about the program's dependencies.
add_executable(minimal_cpp_publisher src/minimal_cpp_publisher.cpp)
ament_target_dependencies(minimal_cpp_publisher ${dependencies})

add_executable(minimal_cpp_subscriber src/minimal_cpp_subscriber.cpp)
ament_target_dependencies(minimal_cpp_subscriber ${dependencies})

# Copy necessary files to designated locations in the project
install (
  DIRECTORY cobot_arm_examples scripts
  DESTINATION share/${PROJECT_NAME}
)

install(
  DIRECTORY include/
  DESTINATION include
)

# Install cpp executables
install(
  TARGETS
  minimal_cpp_publisher
  minimal_cpp_subscriber
  DESTINATION lib/${PROJECT_NAME}
)

# Install Python modules for import
ament_python_install_package(${PROJECT_NAME})

# Install Python executables
install(
  PROGRAMS
  scripts/minimal_py_publisher.py
  scripts/minimal_py_subscriber.py
  scripts/minimal_py_parameters.py
  #scripts/example3.py
  #scripts/example4.py
  #scripts/example5.py
  #scripts/example6.py
  #scripts/example7.py
  DESTINATION lib/${PROJECT_NAME}
)

# Automates the process of setting up linting for the package, which
# is the process of running tools that analyze the code for potential
# errors, style issues, and other discrepancies that do not adhere to
# specified coding standards or best practices.
if(BUILD_TESTING)
  find_package(ament_lint_auto REQUIRED)
  # the following line skips the linter which checks for copyrights
  # comment the line when a copyright and license is added to all source files
  set(ament_cmake_copyright_FOUND TRUE)
  # the following line skips cpplint (only works in a git repo)
  # comment the line when this package is in a git repo and when
  # a copyright and license is added to all source files
  set(ament_cmake_cpplint_FOUND TRUE)
  ament_lint_auto_find_test_dependencies()
endif()

# Used to export include directories of a package so that they can be easily
# included by other packages that depend on this package.
ament_export_include_directories(include)

# Generate and install all the necessary CMake and environment hooks that 
# allow other packages to find and use this package.
ament_package()

Build the Workspace

Open a new terminal window, and type the following commands:

cd ~/ros2_ws/
colcon build
source ~/.bashrc

Run the Node 

In this section, we will finally run our node. Open a terminal window, and type:

ros2 run cobot_arm_examples minimal_py_parameters.py

Now, press Enter.

Open a new terminal window.

What are the currently active nodes?

ros2 node list
1-ros2-node-list

List all the parameters for all nodes in the ROS2 system.

ros2 param list
2-ros2-param-list

Retrieve the value of a specific parameter for a node.

ros2 param get /minimal_parameter_node robot_name
ros2 param get /minimal_parameter_node velocity_limit
3-ros2-param-get

Set the value of a specific parameter for a specified node.

ros2 param set /minimal_parameter_node robot_name '!!str New Automatic Addison Bot'

You should see “Set parameter successful”.

4-name-change-successful

Double check the new value:

ros2 param get /minimal_parameter_node robot_name
ros2 param set /minimal_parameter_node velocity_limit 1.0
5-parameter-has-changed
6-new-velocity-limit

Now go back to the terminals where your scripts are running and press CTRL + C to stop the execution.

To clear the terminal window, type:

clear

That’s it! Keep building!

Sensor Fusion Using the Robot Localization Package – ROS 2

In this tutorial, I will show you how to set up the robot_localization ROS 2 package on a simulated mobile robot. We will use the robot_localization package to fuse odometry data from the /wheel/odometry topic with IMU data from the /imu/data topic to provide locally accurate, smooth odometry estimates. Wheels can slip, so using the robot_localization package can help correct for this.

This tutorial is the third tutorial in my Ultimate Guide to the ROS 2 Navigation Stack (also known as Nav2).

You can get the entire code for this project here.

If you are using ROS Galactic or newer, you can get the code here.

Let’s get started!

Prerequisites 

You have completed the first two tutorials of this series:

  1. How to Create a Simulated Mobile Robot in ROS 2 Using URDF
  2. Set Up the Odometry for a Simulated Mobile Robot in ROS 2

About the Robot Localization Package

We will configure the robot_localization package to use an Extended Kalman Filter (ekf_node) to fuse the data from sensor inputs. These sensor inputs come from the IMU Gazebo plugin and the differential drive Gazebo plugin that are defined in our SDF file.

In a real robotics project, instead of simulated IMU and odometry data, we would use data from an IMU sensor like the BNO055 and wheel encoders, respectively.

 The ekf_node will subscribe to the following topics (ROS message types are in parentheses):

  • /wheel/odometry :  Position and velocity estimate based on the information from the differential drive Gazebo plugin (in a real robotics project this would be information drawn from wheel encoder tick counts). The orientation is in quaternion format. (nav_msgs/Odometry)
  • /imu/data : Data from the Inertial Measurement Unit (IMU) sensor Gazebo plugin (sensor_msgs/Imu.msg)

This node will publish data to the following topics:

  • /odometry/filtered : The smoothed odometry information (nav_msgs/Odometry)
  • /tf : Coordinate transform from the odom frame (parent) to the base_footprint frame (child). To learn about coordinate frames in ROS, check out this post.

Install the Robot Localization Package

Let’s begin by installing the robot_localization package. Open a new terminal window, and type the following command:

sudo apt install ros-foxy-robot-localization

If you are using a newer version of ROS 2 like ROS 2 Humble, type the following:

 sudo apt install ros-humble-robot-localization 

Instead of the commands above, you can also type the following command directly into the terminal. It will automatically detect your ROS 2 distribution:

sudo apt install ros-$ROS_DISTRO-robot-localization

If you are using ROS 2 Galactic and want to build from the source instead of using the off-the-shelf packages above, you will need to download the robot_localization package to your workspace.

cd ~/dev_ws/src
git clone -b fix/galactic/load_parameters https://github.com/nobleo/robot_localization.git

The reason you need to download that package above is because the Navigation Stack might throw the following exception if you don’t:

[ekf_node-1] terminate called after throwing an instance of ‘rclcpp::ParameterTypeException’ [ekf_node-1] what(): expected [string] got [not set]

cd ..
colcon build

Set the Configuration Parameters

We now need to specify the configuration parameters of the ekf_node by creating a YAML file.

Open a new terminal window, and type:

colcon_cd basic_mobile_robot
cd config
gedit ekf.yaml

Copy and paste this code inside the YAML file.

Save and close the file.

You can get a complete description of all the parameters on this page. Also you can check out this link on GitHub for a sample ekf.yaml file.

Create a Launch File

Now go to your launch folder. Open a new terminal window, and type:

colcon_cd basic_mobile_robot
cd launch
gedit basic_mobile_bot_v3.launch.py

Copy and paste this code into the file.

If you are using ROS 2 Galactic or newer, your code is here.

Save the file, and close it.

Move to the package.

colcon_cd basic_mobile_robot

Open the package.xml file.

gedit package.xml file.

Copy and paste this code into the file.

Save the file, and close it.

Open the CMakeLists.txt file.

gedit CMakeLists.txt

Copy and paste this code into the file.

Save the file, and close it.

Build the Package

Now build the package by opening a terminal window, and typing the following command:

cd ~/dev_ws
colcon build

Launch the Robot

Open a new terminal, and launch the robot.

cd ~/dev_ws/
ros2 launch basic_mobile_robot basic_mobile_bot_v3.launch.py

It might take a while for Gazebo and RViz to load, so be patient.

To see the active topics, open a terminal window, and type:

ros2 topic list

To see more information about the topics, execute:

ros2 topic info /imu/data
ros2 topic info /wheel/odometry

You should see an output similar to below:

16-ros2-topic-info-imu-wheel

Both topics have 1 publisher and 1 subscriber.

To see the output of the robot localization package (i.e. the Extended Kalman Filter (EKF)), type:

ros2 topic echo /odometry/filtered
16-zz-odometry-filtered

I will move my robot in the reverse direction using the rqt_robot_steering GUI. Open a new terminal window, and type:

rqt_robot_steering

If you are using ROS 2 Galactic or newer, type:

sudo apt-get install ros-galactic-rqt-robot-steering

Where the syntax is:

sudo apt-get install ros-<ros-distribution>-rqt-robot-steering

Then type:

ros2 run rqt_robot_steering rqt_robot_steering --force-discover

Move the sliders to move the robot.

17-move-the-sliders

We can see the output of the odom -> base_footprint transform by typing the following command:

ros2 run tf2_ros tf2_echo odom base_footprint

Let’s see the active nodes.

ros2 node list
18-ros2-node-list

Let’s check out the ekf_node (named ekf_filter_node).

ros2 node info /ekf_filter_node
19-filter-node-info

Let’s check out the ROS node graph.

rqt_graph

Click the blue circular arrow in the upper left to refresh the node graph. Also select “Nodes/Topics (all)”.

20-ros-node-graph

To see the coordinate frames, type the following command in a terminal window.

ros2 run tf2_tools view_frames.py

If you are using ROS 2 Galactic or newer, type:

ros2 run tf2_tools view_frames

In the current working directory, you will have a file called frames.pdf. Open that file.

evince frames.pdf

Here is what my coordinate transform (i.e. tf) tree looks like:

21-view-coordinate-frames

You can see that the parent frame is the odom frame. The odom frame is the initial position and orientation of the robot. Every other frame below that is a child of the odom frame.

Later, we will add a map frame. The map frame will be the parent frame of the odom frame.

Finally, in RViz, under Global Options, change Fixed Frame to odom.

22-fixed-frame-to-odom

Open the steering tool again.

rqt_robot_steering

If you move the robot around using the sliders, you will see the robot move in both RViz and Gazebo.

gazebo-and-rviz-1

That’s it!

In the next tutorial, I will show you how to add LIDAR to your robot so that you can map the environment and detect obstacles.

How to Succeed in Technology: The 10,000 Experiment Rule

Introduction

In a world obsessed with mastery and success, the 10,000-hour rule has long been heralded as the golden standard for achieving expertise in any field. Popularized by Malcolm Gladwell in his book “Outliers,” this rule suggests that with 10,000 hours of dedicated practice, anyone can master a skill. However, in the fast-paced and ever-evolving landscape of technology, a new paradigm is emerging as a more effective blueprint for innovation and success: the 10,000-experiment rule.

Introduced by Michael Simmons in his thought-provoking article on Medium, “Forget The 10,000-Hour Rule; Edison, Bezos, & Zuckerberg Follow The 10,000-Experiment Rule,” this new rule shifts the focus from the quantity of time spent practicing to the number of experiments conducted. This approach champions experimentation, quick learning, and the ability to adapt and pivot as the cornerstones of technological innovation and personal growth. Through this lens, the stories of Thomas Edison’s relentless experimentation, Jeff Bezos’s innovative leadership at Amazon, and Mark Zuckerberg’s rapid iteration at Facebook take on new significance. They exemplify how embracing a culture of experimentation can lead to unprecedented achievements.

As we dive deeper into the essence of the 10,000-experiment rule, we will explore its foundations, its scientific backing, and its application across various domains of technology, with a special focus on robotics. By understanding and applying this rule, individuals and organizations alike can unlock new pathways to innovation and success in the technological realm.

The Evolution from Hours to Experiments

The traditional 10,000-hour rule, while a useful guideline for developing skill through practice, presents a linear approach to mastery that overlooks the complex, non-linear nature of innovation. In contrast, the 10,000-experiment rule represents a paradigm shift, focusing on the iterative process of trial, error, and learning. This approach is particularly resonant in the field of technology, where rapid advancements and unpredictable challenges require a more flexible and adaptive mindset.

In his 2018 CNBC interview, Bezos articulated the essence of this experimental approach:

To be innovative you have to experiment. If you want to have more inventions, you need to do more experiments per week per month per year per decade. It’s that simple. You cannot invent without experimenting. And here’s the other thing about experiments…lots of them fail. If you know it’s going to work in advance it is not an experiment.

Jeff Bezos

Also, keep in mind that when you experiment, you have to be prepared for many failures. Bezos mentions this in his 2016 Annual Letter to Shareholders:

To invent you have to experiment, and if you know in advance that it’s going to work, it’s not an experiment. Most large organizations embrace the idea of invention, but are not willing to suffer the string of failed experiments necessary to get there. Outsized returns often come from betting against conventional wisdom, and conventional wisdom is usually right. Given a ten percent chance of a 100 times payoff, you should take that bet every time. But you’re still going to be wrong nine times out of ten.

Jeff Bezos

Bezos’s emphasis on experimentation as a core strategy highlights the critical role that embracing failure and learning plays in driving innovation. Under his leadership, Amazon has become a prime example of how a culture of experimentation can lead to groundbreaking innovations, from AWS to Alexa.

Thomas Edison, often hailed as one of the greatest inventors in history, exemplifies the 10,000-experiment rule long before it was formally articulated. Edison’s approach to invention was fundamentally experimental, famously remarking, “I have not failed. I’ve just found 10,000 ways that won’t work.” His work on the electric light bulb, phonograph, and motion picture camera, among countless other inventions, showcases the power of persistence and the willingness to embrace failure as a stepping stone to success.

The shift from hours to experiments encourages a mindset of curiosity, resilience, and openness to failure. It suggests that success in technology and innovation is not merely a function of time spent, but rather the quality and quantity of experiments conducted. This approach fosters a culture of continuous learning and adaptation, essential qualities in the fast-evolving tech landscape.

By embracing the 10,000-experiment rule, individuals and organizations can unlock a more dynamic and effective pathway to innovation. This rule champions the idea that through a systematic approach to experimentation, one can navigate the complexities of technology and emerge with novel solutions and breakthroughs.

Scientific Backing: The Meta-Analysis of Deliberate Practice

The limitations of the 10,000-hour rule are further illuminated by a comprehensive meta-analysis on deliberate practice and performance across various domains, including music, games, sports, education, and professions. This study, which examined the effects of deliberate practice on performance, found that while practice is undoubtedly important, its overall contribution to performance varies significantly across disciplines.

In domains where performance is highly predictable and structured, such as classical music and chess, deliberate practice accounted for a substantial portion of variance in performance. However, in less structured and more dynamic fields, such as technology and entrepreneurship, the impact of deliberate practice was notably smaller:

“We found that deliberate practice explained 26% of the variance in performance for games, 21% for music, 18% for sports, 4% for education, and less than 1% for professions.

This finding suggests that while honing specific skills is important, the ability to innovate, adapt, and experiment plays a crucial role in achieving success in these fields.

The implications of this research underscore the value of the 10,000-experiment rule. In the realm of technology, where the landscape is characterized by rapid change and uncertainty, the capacity to learn from experiments—not just from repeated practice—is paramount. This approach aligns with the idea that success in technology hinges on the ability to navigate ambiguity, test hypotheses, and pivot based on feedback and outcomes.

Case Studies of Success Through Experimentation

The power of the 10,000-experiment rule is vividly illustrated in the stories of tech luminaries and companies that have placed experimentation at the heart of their success. These case studies not only demonstrate the rule’s effectiveness but also inspire a culture of innovation and resilience.

Thomas Edison: The Quintessential Experimenter

Thomas Edison’s work is perhaps the most iconic example of success through experimentation. Edison’s philosophy of “genius is one percent inspiration, ninety-nine percent perspiration” reflects his commitment to the iterative process of innovation. His development of the electric light bulb involved over a thousand experiments, a journey marked by setbacks, learning, and eventual triumph. Edison’s persistence and willingness to learn from each experiment laid the groundwork for modern electric lighting and numerous other technologies, embodying the essence of the 10,000-experiment rule.

Amazon: Cultivating an Experimentation Culture

Under Jeff Bezos’s leadership, Amazon has become synonymous with innovation, largely due to its embrace of experimentation. Amazon’s foray into cloud computing with AWS, its development of the Kindle, and its exploration of AI through Alexa are outcomes of its experimental culture. Bezos’s approach—viewing every failure as an opportunity to learn and every experiment as a step toward discovery—has propelled Amazon into new markets and technologies, illustrating the transformative power of the 10,000-experiment rule in corporate innovation.

Facebook: Rapid Iteration and Growth

Mark Zuckerberg’s Facebook has similarly leveraged the power of experimentation to evolve from a college networking site into a global social media platform. Facebook’s rapid iteration on features, constant A/B testing, and openness to pivoting based on user feedback have been instrumental in its growth and adaptability. This commitment to experimentation has enabled Facebook to stay relevant amidst changing social media landscapes and user preferences.

Applying the 10,000 Experiment Rule in Robotics

Robotics is a field that epitomizes the intersection of complex hardware, sophisticated software, and intricate real-world interactions. The 10,000-experiment rule finds a fertile ground in robotics, where practical, hands-on experimentation and iterative learning are key to innovation and breakthroughs. 

Using ROS 2 (Robot Operating System 2) significantly enhances the capacity for such experimentation, providing a unified and flexible framework for robotics development.

Iterative Design and Testing with ROS 2

The iterative design process in robotics is crucial for developing effective and efficient systems. ROS 2 facilitates this process by offering advanced tools for building and testing robotic applications. Its modular architecture allows roboticists to experiment with different configurations and functionalities easily, streamlining the process of learning from each iteration. By leveraging ROS 2’s capabilities, developers can quickly prototype ideas, test hypotheses, and refine their designs based on real-world feedback.

Simulation-Based Testing Enhanced by ROS 2

ROS 2 integrates seamlessly with powerful simulation tools, such as Gazebo and NVIDIA Isaac Sim, enabling developers to conduct thousands of simulated experiments efficiently. These simulations are invaluable for exploring the behavior of robotic systems under a wide range of conditions without the time and cost associated with physical prototypes. By utilizing ROS 2 in simulation-based testing, researchers can accelerate the experimentation process, rapidly iterating on design and software algorithms to identify promising approaches before real-world implementation.

Real-World Experimentation and ROS 2

When transitioning from simulation to real-world testing, ROS 2’s robustness and flexibility become even more beneficial. Its support for diverse hardware and real-time communication allows for extensive real-world experimentation, critical for refining robot designs and ensuring they can handle the complexities of their intended environments. ROS 2’s ecosystem encourages a collaborative approach to experimentation, where developers can share insights, tools, and best practices, further accelerating innovation in robotics.

Leveraging ROS 2 for the 10,000 Experiment Rule

ROS 2 is designed to support the rapid iteration and flexibility required by the 10,000-experiment rule. Its features enable roboticists to:

  • Prototype Quickly: Developers can use ROS 2 to build and test new ideas swiftly, reducing the time from concept to experimentation.
  • Analyze and Iterate: With ROS 2, it’s easier to collect and analyze data from experiments, facilitating a deeper understanding of each trial and informing subsequent iterations.
  • Collaborate and Share: The ROS 2 community encourages sharing of software, tools, and best practices, making it easier for roboticists to learn from each other’s experiments.

Practical Tips for Implementing the 10,000 Experiment Rule

Adopting the 10,000-experiment rule requires a strategic approach to experimentation. Here are some practical tips for individuals and organizations looking to embrace this mindset:

  • Document Everything: Keep detailed records of each experiment, including the hypothesis, methodology, results, and learnings. This documentation is invaluable for tracking progress and informing future experiments.
  • Embrace Failure: View each failed experiment as a learning opportunity. Analyzing why an experiment didn’t work is often more informative than a successful outcome.
  • Foster a Culture of Curiosity: Encourage team members to ask questions, propose experiments, and explore new ideas. A supportive environment that values curiosity and risk-taking is essential for innovation.
  • Leverage Technology: Utilize software and tools designed for managing experiments. These can help organize data, track progress, and analyze results, making the experimentation process more efficient and effective.

Conclusion

The 10,000-experiment rule offers a compelling framework for achieving success in technology and beyond. By shifting the focus from sheer hours of practice to the quality and quantity of experiments, individuals and organizations can foster a culture of innovation, resilience, and continuous learning. The stories of Edison, Bezos, Zuckerberg, and countless others in the field of technology underscore the transformative power of this approach. As we look to the future, embracing the mindset of experimentation will be key to navigating the complexities of technology and unlocking new realms of possibility. Let the journey of 10,000 experiments begin.